The existence of large-scale image databases of the world opens up the possibility of recognizing one's location by simply taking a photo of the nearest street corner or store-front and finding the most similar image in a database.
However, when this database consists of millions of images of the world, the problem of efficiently searching for a matching image becomes difficult. The standard approach to image matching is to convert each image to a set of scale and rotation invariant feature descriptors (which are often simply referred to as features). In large databases this approach runs into storage space and search time problems as there could be tens of millions of features to deal with.